Another healthy report on consumer from Visa on US (+10%) & International (+9%) spend with credit modestly outpacing debit. $V points out consistency and strength in spend. While higher end is spending fastest, lower end also growing steadily. $MA $AXP $XLY $XLF $COF $JPM $BAC

For those who were adults during the 2008 financial crisis, what was your holy crap, the economy is actually crashing moment?

For those who were adults during the 2008 financial crisis, what was your holy crap, the economy is actually crashing moment?

From $TMUS @JohnSaw "A satellite hundreds of kilometers above Earth faces approximately 50 dB more path loss than a terrestrial cell site just a kilometer away. Moving from 2 GHz to 800 MHz recovers only a few dB of that enormous disadvantage." linkedin.com/pulse/spectrum-…

Every investment decision I've ever made has been informed by a Excel spreadsheet. A financial model that serves as a business simulation and a translation layer between research & insight. The model ultimately is a place to quantify my variant perception and quantify the downside if I am wrong, the two critical pillars of any good public market investment thesis. One of my PM friends says his analysts spend all day with "their noses in models", identifying and researching the hinge variables in a business. This is the game of alpha generation, simple not easy. A cornerstone of the hedge fund interview process is the modeling test, and hedge funds routinely hire ex bankers & private equity for this skill. (A certain hedge fund founder is rumored to have fired analysts who tried to repurpose sell-side models as their own.) When you peel back the onion on Tier 1 fundamental research process, four years since the Chat GPT 3.5 moment, AI adoption is slow & uneven with the median investment process far from transformed (despite an exceptional amount of tokens burned...). Why is that? To me, it's simple. The "bar to impress" a public equity investor with AI has to start with the financial model architecture as the nerve center of the investment process. Almost every AI workflow for investors I've seen is a "hey this is sort of interesting", but not a "you will take this from me over my dead body" workflow. An agent that can summarize your morning inbox well is convenient but won't bend the curve of alpha. An automated, auditable, full-stack research system built around an architecture of Excel models that can help me do the rigor and depth of investment research in 3 days that used to take me 3 weeks? That changes everything. Financial modeling as the foundational layer in that workflow, the critical unlock to truly enact a full end to end research & risk system. Yes, it is productivity, but ultimately it is more about speed to insight and surfacing the right insight to the decision maker at the right time (this is what great analysts do). "But judgment will still sit with the PM, right?" What is investment judgment if not gathering and weighing as many unique insights about a business as possible then continuously comparing that view to market implied expectations? "Brett, but won't modeling move to JSON?". I've heard selective instance of that. Perhaps eventually. But Excel is just this beautiful, auditable, sharable, trusted substrate on Wall Street. It won't die a quick death. The challenge investors have of reinventing their investment process while running risk is materially different than software engineers adopting coding agents. Until September 22nd when Opus 5.5 was released, the technology wasn't there. It's here now. For this simple reason, I believe we are entering a period of accelerated adoption of AI in Finance, particularly in public equity investment process. I am having conversations with firms who are interested in co-developing and enacting this vision for their investment teams. If this resonates with you, please DM.